Summary of Self-improving Diffusion Models with Synthetic Data, by Sina Alemohammad et al.
Self-Improving Diffusion Models with Synthetic Databy Sina Alemohammad, Ahmed Imtiaz Humayun, Shruti Agarwal, John Collomosse,…
Summary of Addressing Common Misinterpretations Of Kart and Uat in Neural Network Literature, by Vugar Ismailov
Addressing common misinterpretations of KART and UAT in neural network literatureby Vugar IsmailovFirst submitted to…
Summary of Tg-phynn: An Enhanced Physically-aware Graph Neural Network Framework For Forecasting Spatio-temporal Data, by Zakaria Elabid et al.
TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Databy Zakaria Elabid, Lena…
Summary of Do Graph Neural Networks Work For High Entropy Alloys?, by Hengrui Zhang et al.
Do Graph Neural Networks Work for High Entropy Alloys?by Hengrui Zhang, Ruishu Huang, Jie Chen,…
Summary of Tempokgat: a Novel Graph Attention Network Approach For Temporal Graph Analysis, by Lena Sasal et al.
TempoKGAT: A Novel Graph Attention Network Approach for Temporal Graph Analysisby Lena Sasal, Daniel Busby,…
Summary of Illuminating the Diversity-fitness Trade-off in Black-box Optimization, by Maria Laura Santoni et al.
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimizationby Maria Laura Santoni, Elena Raponi, Aneta Neumann, Frank…
Summary of Deepspoc: a Deep Learning-based Pde Solver Governed by Sequential Propagation Of Chaos, By Kai Du et al.
DeepSPoC: A Deep Learning-Based PDE Solver Governed by Sequential Propagation of Chaosby Kai Du, Yongle…
Summary of Spectral Informed Neural Network: An Efficient and Low-memory Pinn, by Tianchi Yu et al.
Spectral Informed Neural Network: An Efficient and Low-Memory PINNby Tianchi Yu, Yiming Qi, Ivan Oseledets,…
Summary of A Comparative Study Of Hyperparameter Tuning Methods, by Subhasis Dasgupta et al.
A Comparative Study of Hyperparameter Tuning Methodsby Subhasis Dasgupta, Jaydip SenFirst submitted to arxiv on:…
Summary of Gradient-free Variational Learning with Conditional Mixture Networks, by Conor Heins et al.
Gradient-free variational learning with conditional mixture networksby Conor Heins, Hao Wu, Dimitrije Markovic, Alexander Tschantz,…